# Wrangle data for plotting
iris_bar_df <- iris %>%
group_by(Species) %>%
summarise(Sepal_Width = mean(Sepal.Width)) %>%
ungroup()
# Create basic bar chart (left)
plotly_iris_bar <- plot_ly(iris_bar_df,
x = ~Species,
y = ~Sepal_Width,
type = "bar")
# Create formatted chart (right)
plotly_iris_bar_formatted <- plot_ly(iris_bar_df,
x = ~Species,
y = ~Sepal_Width,
type = "bar",
marker = list(color = nice_colours[["bold_teal_100"]],
line = list(color = nice_colours[['black_100']], width = 1.5)))
plotly_iris_bar
plotly_iris_bar_formatted
plotly_iris_bar_themed <- plotly_iris_bar_formatted %>%
nice_plotly_theme(chart_type = "vertical_bar",
x_title = "Species",
y_title = "Sepal Width")
Alt text (text description of message chart is showing. Mark chart as
decorative)
Source: []
Download the data for Figure x (CSV, 5.0KB).
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Saving in other file formats (e.g. .svg or .jpg) is more complicated and requires installation of another piece of software. Read the Plotly documentation on exporting graphs as static images in R.
# Load in DOACs data
doacs_df <- readr::read_csv(here::here("DOACs_data.csv"),
col_types = "Dcccccdddd")
Use chart_type = “horizontal_bar” for histograms.
# Create chart
plotly_hist <- iris %>%
plot_ly(x = ~Sepal.Width,
color = ~Species,
# Use NICE colours - remove colour names so it maps correctly
colors = unname(nice_palettes[["discrete"]][1:3]),
type = "histogram",
# Black outline for bars
marker = list(line = list(color = nice_colours[['black_100']], width = 1.5)),
# Set bin width
xbins = list(size = 0.2,
start = 1.9,
end = 4.5)) %>%
# Stack bars
layout(barmode = "stack") %>%
# Add NICE theme and set axis titles
nice_plotly_theme(chart_type = "vertical_bar",
x_title = "Sepal width",
y_title = "Frequency")
plotly_hist
# Category order differs from ggplot but no big difference
economics_long %>%
filter(variable != "pop") %>%
plot_ly(x = ~date,
y = ~value01,
color = ~variable,
colors = c("#00436C", "#D07B4D", "#000000", "#37906D"),
type = "scatter",
mode = "lines") %>%
nice_plotly_theme(chart_type = "line")
plot1 <- mpg %>%
filter(manufacturer %in% c("hyundai", "nissan", "toyota")) %>%
count(manufacturer) %>%
plot_ly(y = ~manufacturer,
x = ~n,
color = ~manufacturer,
type = "bar",
orientation = "h",
marker = list(line = list(color = '#000000', width = 1.5)))
nice_plotly_theme(plot1, chart_type = "horizontal_bar")
mpg %>%
filter(manufacturer %in% c("hyundai", "nissan", "toyota")) %>%
count(manufacturer) %>%
plot_ly(y = ~manufacturer,
x = ~n,
color = ~manufacturer,
type = "bar",
orientation = "h",
marker = list(line = list(color = '#000000', width = 1.5))) %>%
nice_plotly_theme(chart_type = "horizontal_bar")
mpg %>%
filter(manufacturer %in% c("hyundai", "nissan", "toyota")) %>%
count(manufacturer) %>%
plot_ly(x = ~manufacturer,
y = ~n,
color = ~manufacturer,
type = "bar",
marker = list(line = list(color = '#000000', width = 1.5))) %>%
nice_plotly_theme(chart_type = "vertical_bar")